周惠成

个人信息Personal Information

教授

博士生导师

硕士生导师

性别:男

毕业院校:大连理工大学

学位:博士

所在单位:水利工程系

学科:水文学及水资源. 工程管理

办公地点:实验3#-435

联系方式:电话:13804245837 QQ:2246578293 微信:dutwaterzhou

电子邮箱:hczhou@dlut.edu.cn

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Flash Flood Forecasting Based on Long Short-Term Memory Networks

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论文类型:期刊论文

第一作者:Song, Tianyu

通讯作者:Ding, W (reprint author), Dalian Univ Technol, Sch Hydraul Engn, Dalian 116024, Peoples R China.

合写作者:Ding, Wei,Wu, Jian,Liu, Haixing,Zhou, Huicheng,Chu, Jinggang

发表时间:2020-01-01

发表刊物:WATER

收录刊物:EI、SCIE

卷号:12

期号:1

关键字:flash flood forecasting; long short-term memory; recurrent neural networks; machine learning

摘要:Flash floods occur frequently and distribute widely in mountainous areas because of complex geographic and geomorphic conditions and various climate types. Effective flash flood forecasting with useful lead times remains a challenge due to its high burstiness and short response time. Recently, machine learning has led to substantial changes across many areas of study. In hydrology, the advent of novel machine learning methods has started to encourage novel applications or substantially improve old ones. This study aims to establish a discharge forecasting model based on Long Short-Term Memory (LSTM) networks for flash flood forecasting in mountainous catchments. The proposed LSTM flood forecasting (LSTM-FF) model is composed of T multivariate single-step LSTM networks and takes spatial and temporal dynamics information of observed and forecast rainfall and early discharge as inputs. The case study in Anhe revealed that the proposed models can effectively predict flash floods, especially the qualified rates (the ratio of the number of qualified events to the total number of flood events) of large flood events are above 94.7% at 1-5 h lead time and range from 84.2% to 89.5% at 6-10 h lead-time. For the large flood simulation, the small flood events can help the LSTM-FF model to explore a better rainfall-runoff relationship. The impact analysis of weights in the LSTM network structures shows that the discharge input plays a more obvious role in the 1-h LSTM network and the effect decreases with the lead-time. Meanwhile, in the adjacent lead-time, the LSTM networks explored a similar relationship between input and output. The study provides a new approach for flash flood forecasting and the highly accurate forecast contributes to prepare for and mitigate disasters.